The journal Regional interuniversity compendium of scientific works "System technologies" was established in January 1997, is published 6 times per year. According to the order of the Ministry of Education and Science of Ukraine No. 1643 dated December 28, 2019, the regional intercollegiate collection of scientific works "System Technologies" is included in the List of scientific professional publications of Ukraine, category "Б". Knowledge area is technical sciences.
The National metallurgical academy of Ukraine
Empirical determination of the minimum sufficient training sample size for machine learning models at a given error level
This article addresses the problem of empirically determining the minimum sufficient training sample size for machine learning regression models in ore sensor sorting systems. The relevance of this topic stems from the significant costs associated with creating representative datasets in the mining industry. The problem lies in the need to transition from a qualitative analysis of the learning curve to a quantitative assessment of the sufficient sample size for a given error level. The aim of the study is to evaluate a hierarchy of approaches: learning curve – parametric power-law extrapolation – GP-based learning-type curve. Methods: 10-fold GroupKFold cross-validation, Bayesian hyperparameter tuning (Optuna), nonlinear regression, Gaussian processes. Results: R² = 0.93 on test folds; the minimum sufficient sample size for RMSE ≤ 12 was estimated in the range of 559–810 observations. Key conclusion: the proposed method allows for a well-founded determination of the threshold beyond which further expansion of the sample ceases to yield practical benefits.
Copyright (c) 2026 System technologies https://creativecommons.org/licenses/by/4.0
Method and software for image quality enhancement
Image enhancement remains an important problem because real scenes are captured under different illumination conditions, with varying object positions, blur, noise, and loss of fine details. Classical techniques based on histogram equalization, brightness correction, and filtering are still widely used because of their low computational cost, yet they are often insufficiently adaptive to the scene content. At the same time, deep-learning-based models such as GFPGAN and Real-ESRGAN provide high-quality restoration results for specific categories of images, although their efficiency strongly depends on whether the chosen architecture matches the actual input data. The purpose of this paper is to develop an adaptive method and software for image quality enhancement that combines the digital AIEA method with the GFPGAN and Real-ESRGAN neural-network models and automatically selects the processing route according to the detected image type. The proposed method consists of preliminary image analysis, image-type identification, and content-aware routing to a specialized enhancement algorithm. Portrait images are processed by GFPGAN, which restores facial structure and local details. Blurred or low-detail images are processed by Real-ESRGAN, which improves sharpness, detail reproduction, and effective resolution. Other scene types are enhanced by the AIEA digital method, which performs global tonal correction, contrast improvement, and local detail enhancement. The software was implemented as a desktop application that supports automatic routing, manual algorithm selection, result visualization, and saving of enhanced images. Experimental evaluation was carried out on three image classes: portrait images, blurred or low-detail images, and general-scene images. Quantitative comparison using PSNR and SSIM showed that the best results are obtained when the selected algorithm matches the image content: GFPGAN is the most effective for portraits, Real-ESRGAN is the most effective for blurred or low-detail images, and AIEA provides balanced enhancement for general-scene images. The proposed approach adapts the enhancement route to the image content and combines the advantages of classical digital processing with specialized neural-network models. This improves the flexibility of the software and provides a practical basis for further extension with new processing routes and more accurate image-type identification.
Issues of improving the efficiency of embedded control systemswith a computer vision module
The development of autonomous robotics, UAVs, and Industry 4.0 requires expanding the sensory capabilities of cyber-physical systems through the implementation of computer vision (CV) modules on edge devices [1, 2]. The transfer of CV algorithms is implemented via Edge Computing and TinyML paradigms, utilizing spatio-temporal optimization techniques such as quantization (transition from FP32 to INT8/INT4) and synaptic pruning [3, 4]. Global studies indicate that any compression requires a trade-off between accuracy and latency, and also makes models less robust to external disturbances [5]. Domestic scientists have significant achievements in the fields of machine learning, monitoring, and image compression; however, these issues are primarily considered in the context of offline analysis or data transmission [6, 7]. In the scientific community, there is an inconsistency of approaches: Data Science specialists evaluate models in isolation using static metrics and ignore the impact of compressed network noise on the control system [8]. Meanwhile, automatic control specialists perceive the video sensor as a classic link with deterministic delay, which may not correspond to the stochastic nature of inference [9]. Finding the optimal trade-off between the computational efficiency of the neural network (inference latency) and the performance quality of the automatic control system itself to achieve maximum reliability and efficiency of autonomous devices. Integrating modern computer vision algorithms into resource-limited edge devices leads to a fundamental contradiction. In classical automatic control theory, a video sensor acts as a dynamic link of pure delay, which creates a phase lag and linearly reduces the system's phase stability margin [10]. Exceeding the delay limit inevitably leads to a loss of controllability of the object. To avoid this, developers use aggressive compression to reduce inference time, which restores the phase margin but distorts error statistics [11]. Specifically, bit-depth reduction generates quantization noise (high-frequency jitter), which acts extremely destructively on the differential component of the regulator and causes chaotic oscillations of the actuators [12]. Since classic optimization metrics are not suitable for such cases, the authors propose a comprehensive multi-level methodology (Co-design) that allows integrating compression parameters into the dynamic’s equations. It encompasses six sequential tasks: creating a digital twin using the MuJoCo physics engine in Unity or NVIDIA Isaac Sim environments [13, 14]; synthesis of ideal control to obtain reference data; Vision-in-the-loop simulation with an uncompressed network; optimization and emulation in QEMU/Renode for TinyML testing [15]; hardware implementation of Hardware-in-the-loop on Nvidia Jetson/Raspberry Pi/STM32 controllers; and a final comparative analysis of the impact of compression on overshoot and stability margins. The conducted analysis suggests that increasing the efficiency of embedded ACS with computer vision is a complex interdisciplinary task. Optimizing neural networks solely by machine learning metrics, without considering the physical object's dynamics, is not effective and can destabilize the control loop. The proposed approach, which combines Data Science and control theory methods (from simulation to HIL testing), creates a solid foundation for researching the relationship between hardware compression parameters and control quality indicators. In the future, its implementation will help develop more accurate mathematical descriptions of the control loop, considering variable delay and quantization noise, as well as formulate clear engineering recommendations for developers of autonomous systems.
Uncertainty-based image segmentation using U-Net architecture and Monte Carlo Dropout
Historically, convolutional neural networks have been widely used to solve image object recognition problems, in particular for preliminary damage analysis after natural disasters. The U-Net architecture has become one of the popular solutions for image segmentation; in recent years, many studies and modifications of this model have proposed, using both attention mechanisms and separate modules to improve classification results. At the same time, research into uncertainty assessment and explanation of the operation of segmentation models still need to be deepened, which is why this work is relevant. Although increasing accuracy and efficiency is an important component of any model, in tasks such as preliminary damage and loss analysis, the assessment of confidence, or uncertainty, is one of the decisive factors, since it can be used for further decision-making. The purpose of the study is to build a segmentation model of the modified U-Net architecture with the ASPP module, taking into account uncertainty by multiple runs of the model with different dropout masks (Monter Carlo Dropout) for preliminary analysis of damaged buildings in images. The paper presents a study of the modified U-Net architecture with the replacement of the bottleneck with the ASPP module and the use of Monte Carlo Dropout. Based on the calculation formulas of variance, entropy and mutual information, uncertainty maps were formed, which were used to improve segmentation. It is shown that ASPP increases the accuracy of object shape detection, and the combined loss function contributes to improving segmentation. In the experiments, an increase of 3.8% of the average IoU was achieved, 4% for surviving and 5.3% for destroyed buildings. The uncertainty masks were analyzed and used in the method with two threshold values ​​of confidence and model uncertainty to select the most reliable areas. For the practical implementation of the described methods and approaches, there is a need for their further study.
Design of a secure architecture for a distributed industrial data collection and storage system on the java platform
In the context of industrial digitalization, ensuring the integrity, availability, and security of technological data in the mining industry has become highly relevant. Recent studies indicate that the Security-by-Design concept is a fundamental principle for building modern information systems, while microservices and event-driven architectures enhance scalability and flexibility. Particular attention is also paid to Industrial IoT security, IEC 62443 standards, cryptographic protocols such as TLS, and modern authorization mechanisms. The aim of this study is to design a secure distributed architecture for industrial data collection and storage on the Java platform, ensuring reliable data acquisition, fault-tolerant storage, and controlled access. The research methodology includes systems analysis, threat modeling, architectural design, and analysis of modern distributed system approaches. A microservices-based architecture is proposed, implementing Security-by-Design principles, zone segmentation in accordance with IEC 62443, and the Zero Trust concept. Secure communication is achieved using TLS 1.3 with mutual authentication (mTLS), while access control is implemented via RBAC. Data exchange is organized using an event-driven architecture. To ensure data reliability, a hybrid storage model (offline-first) with replication and synchronization based on Event Sourcing and Change Data Capture is applied. The results demonstrate improved fault tolerance, elimination of single points of failure, and continuous system operation under partial failures. The proposed architecture enhances data transmission security, ensures strict access control, and enables comprehensive auditing. The conclusions confirm the effectiveness of the microservices approach and modern security mechanisms for upgrading industrial systems and integrating them into enterprise IT infrastructures in accordance with Industry 4.0 requirements.
Methodological approaches and information technologies for satellite monitoring of environmental risks in the mining industry
This article examines modern remote sensing technologies and their role in ensuring the environmental safety of mining regions in Ukraine. The mining industry is regarded as a strategic sector which, at the same time, places a significant anthropogenic burden on the environment due to the accumulation of waste in tailings ponds. The challenges of monitoring these sites have become significantly more acute as a result of military operations and restricted access to industrial sites, which makes traditional ground-based surveys impossible. The use of satellite platforms is a key tool for obtaining objective information on the state of the environment in hard-to-reach areas. The aim of this study is to justify methodological approaches to the use of satellite data for the operational monitoring of mining areas and to develop a risk assessment technique based on spectral-temperature indices. The Shcherbakivske tailings pond, which contains radionuclides and toxic compounds of uranium ores, was selected as the object of study. The effectiveness of integrating satellite data for analysing the dynamics of man-made landscapes was analysed. A methodology was developed for using the Soil Moisture Index (SMI), based on a combination of thermal bands and vegetation indices. This approach allows the influence of vegetation to be mitigated and hidden zones of anthropogenic water infiltration to be identified. Based on the analysis of time series, SMI anomalies were recorded beyond the tailings dam, indicating filtration leaks towards agricultural land and risks of contamination of food chains. The introduction of satellite monitoring is essential for overcoming the fragmentation of environmental monitoring systems in Ukraine. The use of temperature data, combined with automated data processing, facilitates the transition to systematic early detection of hazardous processes.
Youth cyberhygiene on social platforms: from analysis of aitm attacks to models of safe behavior
This article presents a comprehensive study of the risks faced by young people in today’s social media environments. The relevance of the study stems from the rapid development of social engineering methods, which are predominantly based on psychological manipulation. A classification of social networks by user age groups is presented, which has enabled the identification of priority attack vectors for each platform. Based on an empirical survey, critical gaps in young people’s digital literacy have been identified: it was found that a significant proportion of respondents are vulnerable to Adversary-in-the-Middle (AiTM) attacks due to an inability to recognise fake domains, as well as ignoring the absence of data encryption protocols. The mechanics of session token interception and subsequent account takeover have been analysed in detail. The practical significance of the study lies in the development of a three-tier protection model, which includes system-wide, platform-specific (Telegram, Instagram, TikTok) and network-related (Wi-Fi security) recommendations. The proposed educational and behavioural approach shifts the focus from purely technical protection measures to fostering active digital resilience in users.
Principles and solutions for creating power and measurement equipment for plasma actuator research
Promising directions of development of aviation technology require use of non-traditional methods of influence on aerodynamic flow. One of such methods is use of plasma actuators. Both theoretical researches and experimental tests demonstrate possibility of expansion of range of laminar flow, increase of angle of attack without flow breakdown and set of other phenomena. Theoretical methods of modeling of this complex system have received significant development. At the same time it is necessary to note both variety of factors in modeling process, and large amount of resources which are required for modeling process. No less significant amount of resources is required for experimental researches in this direction, taking into account both variety of phenomena which will manifest themselves in these processes, and significant difference in characteristic time intervals, inherent in processes which require research. To ensure a controlled and stable barrier plasma discharge, both an appropriate high-potential difference generation device with the ability to control the potential and frequency are required, as well as special measuring equipment capable of operating under these conditions. It is also necessary to take into account the limited technical and financial capabilities in the modern environment. Therefore, the task of creating a controlled high-voltage power supply system for barrier discharge research is relevant. The article describes the design of a regulated high-voltage alternating voltage source, with frequency control and the ability to measure the output current and voltage. The schemes and their description, design features are given. A series of experiments were conducted to determine the operability of all its elements and modules. The design has shown its operability and can be used in a series of laboratory studies on the control of plasma actuators.
Analysis of control methods for thermal processing of canned fruits and vegetables in vertical autoclaves
The paper examines the features of the process of heat treatment of canned fruits and vegetables in vertical autoclaves aimed at preserving their quality and safety for a long time. An analysis of existing methods of controlling the processes of heat treatment of canned fruits and vegetables in vertical autoclaves is carried out. The problems of using traditional proportional-integral-differential (PID) controllers are considered, due to the significant thermal inertia of the object and the complexity of setting it up for different types and masses of loading. Promising approaches to automation are analyzed, in particular model-based predictive control (MPC), which allows optimizing the process directly according to the target indicator of accumulated microbiological lethality (F0) taking into account technological limitations. Special attention is paid to the possibilities of using intelligent methods, such as adaptive fuzzy controllers (Fuzzy-PID) and artificial neural networks (ANN), which perform the function of virtual "soft sensors" for predicting the temperature inside the container. It was found that the use of standard algorithms for such products leads to significant errors in the calculations of accumulated lethality and, as a result, to thermal damage to raw materials (digestion). The results of the analysis substantiate the need to transition to hybrid intelligent control systems to ensure energy efficiency, compensate for the nonlinearity of heat exchange processes and preserve the nutritional value of complex heterogeneous products.
Research into technologies and development of artificial intelligence-based device control system
Analysis of recent studies and publications. Among the existing solutions, commercial ones can be distinguished, which are more focused on the tasks of sports racing, extreme aerial photography and cinematography and in most cases use satellite navigation. In today's realities, with the active development of this direction, there are several special platforms with proposed solutions and software from the manufacturer (for example, the VGI-9 [1] and Zir System [2] platforms). Purpose of research. The purpose of this research is to analyze machine vision and object detection methods, develop and implement an intelligent FPV device control system based on artificial intelligence, which is capable of performing automatic detection, tracking and tracing of selected objects in real time. It is planned to deploy the system at the edge level of the device with subsequent direct installation of the appropriate software. Presentation of the main research material. The main requirements for the control system are performance, accuracy and resource efficiency. The system must provide video stream processing with a frequency of at least 30 displayed frames per second (Frames Per Second, FPS) on the target hardware platform Orange Pi 5. System architecture. The developed device control system is based on the principles of modular architecture with a clear separation of responsibilities between components. The system operates on the principle of pipeline processing using multi-threading to minimize latency. Practical testing of the system. The YOLOv11 model family is represented by five variants of different sizes from nano to extralarge. A combined approach was used to train the object detection model: the model is trained on the large open VisDrone dataset [9], later the model can be further trained on user data that is specific to a particular task. The test bench consists of an Orange Pi 5 Max device and a 13MP camera. In real conditions, the system requires settings in Headless mode with video output to the framebuffer (a memory area for temporary storage of the image before displaying it on the screen). Experimental tests of the developed device control system were conducted to verify the operability of key components and assess their performance. The tests were performed on a static stand without physical integration with the device. Conclusions. In the course of the research, an intelligent FPV device control system based on artificial intelligence was developed and implemented, capable of performing automatic detection and tracking of objects in real time on the Orange Pi 5 edge device with NPU RK3588. A modular multi-threaded architecture was designed with the integration of the MAVLink communication protocol with the ArduPilot flight controller and PID controllers for the autonomous target pursuit mode were implemented. Experimental testing confirmed the system's operability on a static stand, but revealed performance limitations.
Intelligent method for moving object identification based on feature clustering in the mel-frequency cepstral domain
The implementation of intelligent monitoring systems in modern industrial automation and advanced transportation networks requires the development of highly reliable methods for moving object identification. The relevance of this study is driven by the necessity to create systems capable of stable operation under conditions of intense acoustic noise, limited optical visibility, and dynamic environmental parameters. Traditional amplitude-based sound processing methods prove to be ineffective due to their high sensitivity to signal non-stationarity and the Doppler effect, which prevents accurate segmentation of an object’s dynamic states as the distance to the sensor changes. The objective of this work is to develop and scientifically substantiate a method for identifying motion phases based on the complex application of Mel-frequency cepstral analysis and intelligent clustering. The proposed approach is based on the synthesis of Short-Time Fourier Transform) algorithms using the Hamming window to minimize spectral leakage, psychoacoustic filtering based on Mel-scale filters, and Discrete Cosine Transform for feature disintegration and decorrelation in the cepstral domain. The research methodology is based on converting a non-stationary acoustic signal into a time-frequency representation followed by the calculation of Mel-Frequency Cepstral Coefficients. This allowed for the formation of a multidimensional feature matrix that acts as a unique "digital fingerprint" of the object. A significant advantage of this method is its invariance to amplitude (loudness) fluctuations, achieved through logarithmic energy scaling within each critical frequency band. For automatic state recognition, an unsupervised machine learning method – K-means clustering – was applied, allowing for the grouping of feature vectors into compact centroids corresponding to specific motion phases. The scientific novelty of the results lies in the improvement of the dynamic state segmentation process for moving objects by transitioning from time-domain characteristic analysis to analysis within the cepstral feature space. It has been experimentally confirmed that distinct differentiation is possible between the phases of approach, transit (point of closest proximity), and departure of the object. The results demonstrate high computational stability and speed, ensuring the possibility of integrating the method into local microprocessor-based automated control systems, PLCs, and real-time technical diagnostic systems. The practical value of the work lies in the potential to create autonomous acoustic detectors for monitoring industrial equipment and traffic flows without the need for expensive video surveillance systems.
Effect of volume energy density (VED) on the porosity and mechanical properties of Inconel 718 alloy manufactured by laser pwder bed fusion
The work investigates the effect of volumetric energy density (VED) on the formation of porosity and mechanical properties of the heat-resistant nickel alloy Inconel 718 manufactured by the LPBF method. The relevance of the study is due to the fact that in most modern works, the relative density (porosity) of the material is considered as the main criterion for the quality of additively manufactured products, while the effect of VED variation under the condition of achieving the same level of relative density remains insufficiently studied. The test specimens were manufactured at two VED values - 48.7 and 57.6 J/mm3 - ensuring the same high relative density at the level of 99.8%. Metallographic analysis of polished sections with a quantitative assessment of porosity, as well as tensile tests, was carried out. It was established that at the same relative density, the nature of the type and size of pores differs significantly depending on the VED value. At a higher VED value, an increased number of small pores with a size of 2-3 μm is observed, while at a lower VED value, their total number is smaller, but there are single pores of larger size (up to 17-20 μm). It is shown that the change in VED has practically no effect on the temporary resistance. At the same time, a significant effect of VED on plastic characteristics was found: samples manufactured at a VED of 57.6 J/mm3 are characterized by increased values of relative elongation and narrowing compared to samples obtained at a VED of 48.7 J/mm3. The obtained results confirm that assessing the quality of LPBF products only by the relative density indicator is insufficient. Variation in VED can cause different defect morphology and different levels of plastic properties even with the same relative density of the material. This indicates the need for a comprehensive approach to optimizing LPBF modes, taking into account not only density, but also stability and reproducibility of mechanical characteristics.
Prompt engineering for zero-shot land cover classification using multimodal language models on SENTINEL-2 imagery
Multimodal language models (VLMs) enable land cover classification from satellite imagery without labeled training data. This paper, extending previous work [8], analyzes prompt engineering approaches for land cover classification on Sentinel-2 imagery within the ESA WorldCover 2021 taxonomy. The color leakage phenomenon is identified and described, where the model bases its predictions on segmentation mask colors rather than image content. A four-invariant prompt protocol is proposed, including TCI-first ordering, grayscale mask conversion, elimination of color descriptions, and a fixed JSON output format, which removes this effect and increases the format compliance rate (FCR) from ≈60% to 97%. Two inference strategies are compared: Variant A (multi-cluster, mIoU ≈ 7.1%) and Variant B (single-cluster, mIoU ≈ 13.2%) on 10 Sentinel-2 tiles. In Variant B, each segment is processed independently using a binary mask, which simplifies spatial interpretation and reduces inter-segment interference. The highest result (mIoU = 46.2%) is achieved with the UNet-encoder + GPT-4.1 + Variant B configuration, although this corresponds to a single case. Problem Statement. Land cover mapping from satellite imagery is widely used in ecological monitoring, urban planning, and agronomy. Traditional semantic segmentation approaches require large labeled datasets and significant computational resources, especially when adapting to new regions. Recent multimodal language models, including GPT-4.1, Claude 3.7 Sonnet, and Gemini 2.5 Pro, enable zero-shot classification without task-specific training. However, such pipelines introduce specific failure modes, notably the color leakage effect, where predictions depend on segmentation mask colors instead of actual image content. Recent Studies and Publications Analysis. VLMs are increasingly used in remote sensing owing to their capacity for open-vocabulary reasoning over satellite imagery. Yao et al. introduced Falcon, a remote sensing vision-language foundation model; Mall et al. developed RSVLM for satellite image understanding; Li et al. presented RS-CLIP for zero-shot scene classification. Liu et al. proposed RSHBench — a detailed benchmark for diagnosing hallucinations in multimodal LLMs applied to remote sensing. For zero-shot learning, Saha et al. demonstrated improved classification by adapting VLMs with attribute descriptions; Barzilai et al. analysed recipes for improving VLM zero-shot accuracy in remote sensing. In prompt engineering, Wei et al. established chain-of-thought prompting and White et al. catalogued reusable prompt patterns. Geirhos et al. documented shortcut learning in deep networks, providing theoretical grounding for the color leakage phenomenon. Despite these advances, systematic analysis of prompt design for eliminating color artifacts in VLM-based land cover classification remains unstudied. Research Objective. The objective of this study is to improve classification accuracy (mIoU) and structured output correctness (FCR) in zero-shot land cover classification on Sentinel-2 imagery by developing a prompt engineering protocol for multimodal language models that eliminates the color leakage effect and enforces a fixed structure of inputs and outputs. Main Body of Research. A two-stage processing pipeline is used, combining unsupervised segmentation with VLM-based classification under a four-invariant protocol: TCI-first ordering, grayscale mask, no color descriptions, and structured JSON output. Variant A performs classification of all segments in a single request, while Variant B processes each segment independently using a binary mask. This change in formulation improves mIoU from 7.1% to 13.2%. Ablation analysis (n = 5 tiles) shows that the JSON output constraint has the largest impact on FCR, while grayscale mask conversion most effectively reduces color leakage. Per-class analysis indicates that the improvement is primarily driven by the Cropland class (23.4% → 46.9%), whereas spectrally similar vegetation classes degrade. Conclusions. The study addresses the problem of improving classification accuracy (mIoU) and structured output correctness (FCR) in zero-shot land cover classification on Sentinel-2 satellite imagery through the development of a prompt engineering protocol for multimodal language models. The proposed protocol, consisting of four mandatory rules, eliminates the color leakage effect and increases FCR from ≈60% to 97%. It is shown that the use of the single-cluster processing strategy (Variant B), in which each segment is processed independently using a binary mask, improves classification accuracy from 7.1% to 13.2% compared to the multi-cluster strategy (Variant A). This approach eliminates inter-segment context contamination, simplifies segment interpretation for the model, and improves structured output correctness, as each request produces a single JSON object. The highest result (mIoU = 46.2%) is achieved with the UNet-encoder + GPT-4.1 + Variant B configuration; however, this corresponds to a single configuration and is not representative of overall performance across models and segmentation methods.
Regularities of formation of total energy expenditures of pulse-jet bag filters at the stages of manufacturing and operation
The paper examines energy consumption throughout the lifecycle of construction and operation of pulse-jet regenerated bag filters (PJBF) for flue gas dedusting. Experimental data from PJBF developed and implemented by SE ‘’UkrSTC "Energostal" at Ukrainian mining-metallurgical enterprises over the past 30 years were collected, generalized, and analyzed. Obtained dependencies of filtration area, mass, and compressed air consumption on filter productivity enabled determination of actual energy consumption, including energy expenditures for metal production and construction of filter housing and mechanical equipment, energy for filter bags, electricity for fan operation to transport flue gases through the filter, and electricity for compressor operation for air compression in the regeneration system. It was established that annual operational energy expenditures are comparable to energy costs for filter unit production. Both values show similar absolute figures and dependency patterns. This equivalence offers a new perspective on the energy load structure of filtration systems over their lifecycle and highlights parameters significantly affecting total energy intensity.
Modern information technologies for processing and searching digitized documentsin the education of students of the specialty B 13 "Library, information and archival affairs"
The article examines the processes of digitization of documentary heritage and the creation of electronic collections within library and archival institutions. Special attention is paid to the classification of documents subject to digitization, including unique materials, archival and library holdings, manuscripts, highly demanded documents, and materials available in limited quantities. Digitization of such documents is considered an effective approach to preserving original sources while simultaneously providing broader access to information resources. The study describes the technological stages of creating digital copies, including scanning, technical processing of images, quality control, and storage of digital files. Particular emphasis is placed on the creation of preservation copies intended for long-term storage as well as user-oriented electronic versions designed for public access. The article also analyzes the role of information systems in managing digitized materials. Digital resources are described and recorded using specialized databases and metadata standards, which ensure the organization, identification, and retrieval of electronic documents. The use of integrated platforms allows specialists to coordinate the processes of digitization, description, storage, and dissemination of digital materials within a unified technological environment. Special attention is given to the organization of digital collections and the implementation of search tools that enable users to quickly locate relevant documents within electronic libraries and archives. Furthermore, the article highlights the importance of integrating digitization technologies into the educational process for students specializing in Library, Information and Archival Studies (specialty В13). Studying the principles of creating and managing digital collections provides future professionals with the competencies necessary for working with electronic resources, organizing digital archives, and ensuring long-term preservation of documentary heritage. The implementation of such approaches contributes to improving access to cultural and scientific information and strengthens the role of digital technologies in the development of modern information infrastructure.
Local supply of modifying components to the vibration compaction zone of a concrete mixture
The article considers a system for the local supply of cement slurry or liquid modifying components directly into the compaction zone of a concrete mixture. The feasibility of combining deep vibration action with dosed hydraulic introduction of an active liquid phase is substantiated, which makes it possible to improve the formation of a dense and homogeneous concrete structure. A structural and technological scheme of the system is proposed, which includes a tank for preparing cement slurry or modifying liquid, an electric mixer, a pump, a pressure gauge, a safety valve, a throttle valve, a shut-off valve, a pressure line, and an internal vibrator with a nozzle for local liquid injection. The sequence of system operation is considered, which involves preparing a homogeneous suspension, supplying it under pressure to the internal vibrator, and injecting it through nozzles directly into the active compaction zone of the concrete mixture. It is shown that the local introduction of cement slurry or modifying components into the vibration-activated mixture improves aggregate wetting, reduces voids and dry inclusions, improves the contact between cement stone and aggregate, and increases the homogeneity of the concrete structure. It is established that the system efficiency depends on the coordination of pressure, flow rate, nozzle diameter, supply duration, and vibration compaction mode. The obtained results can be used to improve concrete mixture compaction technology, especially for products of complex shape, densely reinforced structures, stiff and low-workability mixtures, as well as for further substantiation of the parameters of local injection systems for liquid modifying components.
Textual api description standard as a tool for improving software testing quality
The rapid adoption of microservice architecture has made application programming interfaces (APIs) the primary integration mechanism in modern software systems. Accordingly, the quality of API testing depends directly on the completeness and structure of API specifications available to testing engineers. In practice, however, the majority of projects document their APIs as informal plain text in corporate knowledge management systems - Confluence, Google Docs, Notion - without adhering to any unified standard. A systematic analysis of four current ISTQB syllabuses (CTFL v4.0.1, CTAL-TAE v2.0, CT-TAS v1.0, CT-AI v1.0) reveals that none of them defines the minimum required content for a textual endpoint description, despite recognising documentation quality as a measurable characteristic (FL-BO4). Existing research confirms the problem: Uddin and Robillard identified "incompleteness" as the most prevalent failure mode across API documentation, while Murphy et al. reported that specifications are "frequently missing, vague, or outdated" in real development teams. Machine-readable formats such as OpenAPI Specification address a different audience and assume technical knowledge of YAML or JSON, leaving the gap in informal human-readable documentation unresolved. The purpose of this study is to develop and validate the Minimal API Description Standard (MADS) - a structured 10-field template for plain-text API endpoint descriptions in corporate documentation tools - and to demonstrate its impact on software testing quality. MADS organises ten fields into four functional blocks: endpoint identification (HTTP method, URL pattern, operation name), input data (request parameters with types and constraints, request body), output data (successful response structure, error codes with conditions), and security context (authentication model, preconditions and business rules, API version). Fields are classified as mandatory or recommended. Each field is justified through convergent evidence from the scientific literature and practical security requirements (OWASP API Security Top 10). Empirical evaluation was conducted across three REST API endpoints of a typical order management service. Test cases were designed using two ISTQB-standard techniques: Boundary Value Analysis (BVA) and Equivalence Partitioning (EP). Three indicators were measured for both an unstructured description (UD) and a MADS-compliant description: the standard Requirement Coverage metric (RC, per ISTQB CTFL v4.0.1 section 5.3.1 and IEEE 829), the applicability of BVA and EP as a binary indicator per parameter, and the total number of test cases. Results show that RC increases from 23% (UD) to 100% (MADS), BVA/EP applicability rises from 25% to 100% of parameters, and the test case count grows from 5 to 26 — a 5.2-fold increase achieved exclusively through structured documentation - a 5.3-fold improvement achieved exclusively through structured documentation, without additional development resources. Response Code Coverage reached zero for all three endpoints under the unstructured condition, meaning negative test scenarios were entirely absent. The study further demonstrates that MADS serves as a structural prerequisite for reliable LLM-based test generation pipelines: structured MADS chunks improve RAG retrieval accuracy and enable deterministic resource access in Model Context Protocol (MCP) agentic architectures. The article proposes that the ISTQB Foundation Level Working Group consider incorporating minimum requirements for informal textual API descriptions into a future revision of the CTFL syllabus. Future research directions include automated MADS compliance validation, empirical correlation studies between MADS adoption and post-release defect rates, and extension of the standard to GraphQL and gRPC APIs.
Software implementation of mathematical models for production-rule knowledge bases in power system dispatch control
This paper analyzes approaches to knowledge-base-driven intelligent information computer systems designed to support the dispatch control of electric power systems under time and resource constraints. Significant results were obtained through concepts of situational management, simulation modeling, and heuristic analysis, as well as the application of pattern recognition methods. The core idea for solving the problem of ensuring power system mode quality is that the bulk of the initial topology and state data is pre-processed, while the direct control actions are determined using relatively simple algorithms based on data concerning the previous normal state and the vector of disturbance parameters. Success in solving power system mode control tasks is largely determined by the representativeness of the mode-quality characteristic parameters. Thus, the search for optimal control actions entails solving the following tasks: recognition and classification of emergency situations; determination of the locations and types of control actions depending on the specific emergency context; and determination of the dosage of dispatch control actions. The study highlights the specific application of the production-rule form of knowledge representation, which is based on the implementation of conjunctive-disjunctive logical operations. The task of building production-rule expert systems is considered on the theoretical basis of Petri nets, which enables the generation of consistent logical decision-making models while accounting for parallelism. The paper examines the task of ensuring the quality of the electric power system in both normal and post-emergency modes by utilizing heuristic methods for situational management and dispatcher decision support. It is proposed to construct the knowledge base using parameter training sets generated according to factorial design matrices. The study proposes an integrated knowledge representation model for the dispatch control domain based on semantic and production networks. The research results can be utilized both at the higher level of dispatch control and in regional power grids during the planning of emergency control measures.
Study of the blade installation angle of a forced-action mixer with a vertical shaft in the “mixer — concrete pump” system
The article considers the influence of the blade installation angle of a forced-action mixer with a vertical shaft on the process of concrete mixture mixing in the “mixer — concrete pump” technological system. A computer model of the mixer was developed in the SOLIDWORKS software package, which made it possible to study the movement of the mixture in the working chamber, evaluate the distribution of particle velocities, and identify zones of insufficient or excessive mixing. A D400 polystyrene concrete mixture with a compressive strength class of B1.0 was adopted as the studied medium, and the blade movement speed during the simulation was 0.66 m/s. The operation of a conventional 100 mm wide blade at installation angles of 20° and 10° was considered, as well as several variants of a modernized vertical blade, including a “boot-shaped” design, a blade with a triangular cutout, and a blade with additional openings. It was established that, at a 20° installation angle of the conventional blade, the maximum velocity of mixture particles reaches 1.65 m/s, which may cause excessive mixing intensity in individual zones, whereas reducing the angle to 10° decreases the maximum velocity to 0.7 m/s and provides more uniform material movement. It is shown that the modernized vertical blade with openings at an installation angle of 10° provides a balanced mixing mode with a maximum velocity of about 0.84 m/s and uniform circulation of the mixture within the chamber volume. The obtained results can be used to substantiate the rational blade installation angle, improve the design of forced-action mixers with a vertical shaft, increase the homogeneity of the concrete mixture, and ensure its stable supply by a concrete pump.
Priority gossip-based synchronization method for distributed datain a group of unmanned aerial vehicles
This paper presents the development and investigation of a priority gossip synchronization method for distributed data in a group of unmanned aerial vehicles (UAVs). The relevance of the topic is driven by the rapid expansion of UAV swarm applications in monitoring, reconnaissance, search and rescue, and logistics tasks, where effective real-time data synchronization between agents is a necessary condition for successful mission execution. Each UAV continuously generates sensor data including telemetry, coordinates, and observation results that must be distributed across the swarm for coordinated decision-making. Existing synchronization methods have significant limitations: the standard gossip protocol does not account for the current state of nodes when selecting an exchange partner, while the Raft consensus algorithm incurs substantial overhead for coordinator assignment and does not ensure fault tolerance upon coordinator failure. The problem lies in the need to develop a synchronization method that ensures fast convergence and minimal data staleness in swarm nodes without a centralized coordinator. The aim of the study is to develop a priority gossip synchronization method based on a node priority function. Each agent selects an exchange partner with probability proportional to its priority value rather than uniformly at random. For verification, computer simulation was conducted for swarms ranging from 50 to 200 UAVs, averaged over 30 independent runs. The results demonstrate a convergence improvement of 2.1–5.6% and lower average data staleness compared to the standard gossip protocol. The Raft algorithm showed the slowest convergence due to coordinator assignment overhead. The proposed method requires no centralized coordinator, is fault-tolerant upon individual agent failure, and adapts naturally to changes in onboard subsystem states.